Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
With the proliferation of networking and data storage technologies, search and retrieval operations are becoming increasingly complex and sought after. Search and retrieval of images is a substantially different process than search for textual data. In searching for images, commonly similarity is used in obtaining results. A contrasting process involving images is authentication, where uniqueness of an image may be attempted to be proven.
One of the approaches employed in image authentication and/or similarity determination is the expectation maximization (EM) technique, which is an iterative process to compute the Maximum Likelihood (ML) estimate in the presence of missing or hidden data. In ML estimation, model parameters are attempted to be estimated for which the observed data are the most likely. Each iteration of the EM technique may include two processes: The E-step, and the M-step. In the expectation, or E-step, the missing data may be estimated given the observed data and current estimate of the model parameters. This may be achieved using conditional expectation. In the M-step, the likelihood function may be maximized under the assumption that the missing data are known. The estimate of the missing data from the E-step may be used in lieu of the actual missing data. Convergence may be assured since the technique is guaranteed to increase the likelihood at each iteration.
Employing the EM technique in image retrieval and authentication has several shortcomings. For example, the EM technique may converge to a local maximum, whereas image data may include multiple local maxima. The EM technique may also suffer from a singularity when the denominator is zero or substantially close to zero. Moreover, a computation time may be undesirably long before convergence and a number of Gaussian models to be used in the technique may be hard to determine.